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flight_delay_anaysis.ipynb (162407, 2019-03-18)

# Application-of-Central-Limit-Theorem This Project is divided into two parts. The first one uses flight-delay dataset and the task is to choose an airline which is most likely to get on or before time
The second Part is a demonstration of Central Limit Theorem and its four aspects.
## Four Aspects of Central Limit Theorem:
1) Sampling Distribution of the mean will be less spread than the values in population from which the sample is drawn.
2) Sampling distribution will be well-modeled by a normal distribution (and will get closer with increasing sample size)
3) Spread of the sampling distribution is related to the spread of population values as mean(population)=mean(sampling distribution) and standard_deviation(population)= standard_deviation(sampling distribution)* sqrt(n) where n is the sample size.
4) Bigger samples lead to a smaller spread in the sampling distribution.

The dataset used can be found on the given link: https://github.com/WillKoehrsen/Data-Analysis/tree/master/univariate_dist/data

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